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Record W2548066263 · doi:10.4018/ijavet.2016100101

Comparing Peer-to-Peer and Individual Learning

2016· article· en· W2548066263 on OpenAlexaff
Patrick Kelly, Larry Katz

Bibliographic record

VenueInternational Journal of Adult Vocational Education and Technology · 2016
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDisadvantagedPsychologyIntervention (counseling)Medical educationPeer groupPopulationMathematics educationPeer learningDevelopmental psychologyMedicine

Abstract

fetched live from OpenAlex

Peer-to-peer (P2P) learning within two distinct groups of disadvantaged adults was studied during a two-hour computer skills workshop. Of interest was whether or not P2P learning with this population was a viable method for increasing performance and confidence. Two qualified instructors at two locations taught the same introductory word-processing workshop to students enrolled in one of three learning intervention groups: P2P, Individual (IND), and No Intervention (NINT). Data was collected through pre- and posttests (all groups), quizzes (P2P and IND groups) and qualitative analysis of P2P group discussions. Quiz results indicated that those in the P2P group gained a better understanding of concepts than participants in the IND group; however, posttest results showed that the understanding was not maintained over time. Confidence in computer skills knowledge increased between the pre and posttest in all treatment groups, regardless of correct or incorrect answers. Analysis of P2P discussions found a significant relationship between the quality of peer discussions and the posttest scores. This study concluded there is a potential benefit of using P2P strategies with disadvantaged adults in the classroom, as confidence is developed and maintained, even as knowledge was not.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.594
Threshold uncertainty score0.394

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.039
GPT teacher head0.403
Teacher spread0.365 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations5
Published2016
Admission routes1
Has abstractyes

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